Treating image prompts like config: a structured brief workflow for Seedream 5.5

Disclosure: I'm the owner of seedream55ai.com, an independent web workspace for the Seedream 5.5 image model. It is a third-party tool and is not affiliated with ByteDance or any model provider.

Most bad AI images I see come from one-line prompts that mix subject, style and layout in a single run-on sentence. When something comes out wrong, you have no idea which part to change. The fix that worked for me is boring and very developer-friendly: treat the prompt like a config file.

1. Split the brief into fields

Before writing any prose, I fill in a small object:

{
  "subject": "matte white ceramic coffee mug, no logo",
  "setting": "light oak table, soft morning window light from the left",
  "camera": "45-degree angle, 50mm look, shallow depth of field",
  "style": "clean e-commerce product photo",
  "text": null,
  "aspect_ratio": "1:1",
  "avoid": ["extra handles", "warped rim", "busy background"]
}

Each field answers one question. If the light is wrong, you edit setting. If the mug looks warped, you add to avoid or tighten subject. Changes become diffs instead of rewrites.

2. Render the object into a prompt

A tiny function turns the object into the sentence the model actually receives:

function renderPrompt(b) {
  const parts = [
    `${b.style} of ${b.subject}`,
    `on ${b.setting}`,
    b.camera,
    b.text ? `with the exact text "${b.text}"` : null,
    b.avoid?.length ? `avoid: ${b.avoid.join(", ")}` : null,
  ];
  return parts.filter(Boolean).join(". ") + ".";
}

Keep the rendered prompt in version control next to the brief. When a result is good, you can reproduce it later; when it is bad, git diff tells you exactly what changed.

3. Change one field per iteration

This is the same discipline as debugging: one variable at a time. My loop is:

1. Generate 2 to 4 variations from the same brief.
2. Pick the closest one and write down what is wrong in one line.
3. Map that line to a single field and edit only that field.

Three or four iterations usually get a usable product shot. Changing everything at once usually gets you a different, equally wrong image.

4. Text in images needs its own field

Posters and labels fail most often on typography. Put the literal string in a dedicated text field, quote it exactly in the rendered prompt, keep it short, and say where it goes ("centered headline at the top"). For bilingual posters, give the English and Chinese strings separately rather than asking the model to translate.

5. Use reference images for edits, not longer prompts

When you already have a photo and only want to change the background or lighting, an image-to-image edit with the original as a reference beats describing the product in more and more words. The brief then only describes the change: "replace background with a plain warm grey studio sweep, keep the product unchanged".

Where I run this

I built Seedream55ai partly to make this loop less painful: text-to-image and reference edits in one place, aspect ratios like 1:1, 16:9 and 9:16, up to four outputs per request, and a credit quote shown before each generation. It is credit-based (packs from $2.99, plans from $7.99/month), so it is not a free unlimited service. The brief-as-config approach above works with any image model though, so steal the template either way.

Comments

[오피셜메일]

주제·조명·문자열을 필드로 나누고 한 번에 한 필드만 바꾸는 방식이 디버깅 흐름과 잘 맞네요. 비교할 때는 브리프 외에 모델 버전과 참조 이미지도 같은 조건인지 함께 적어두면, 문구 변경의 영향과 생성 조건의 영향을 구분하기 쉬울 것 같습니다. 이미지 안의 실제 문장을 별도 필드에 두는 기준도 검수에 유용해 보여요.